Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy; Department of Radiology, Ca' Foncello General Hospital.Piazzale Ospedale 1, 31100, Treviso, Italy.
Eur J Radiol. 2021 Mar;136:109529. doi: 10.1016/j.ejrad.2021.109529. Epub 2021 Jan 7.
Parotid lesions show overlaps of morphological findings, apparent diffusion coefficient (ADC) values and types of time/intensity curve. This research aimed to evaluate the role of diffusion weighted imaging texture analysis in differentiating between benign and malignant parotid lesions and in characterizing pleomorphic adenoma (PA), Warthin tumor (WT), epithelial malignancy (EM), and lymphoma (LY).
Texture analysis of 54 parotid lesions (19 PA, 14 WT, 14 EM, and 7 LY) was performed on ADC map images. An ANOVA test was used to estimate both the difference between benign and malignant lesions and the texture feature differences among PA, WT, EM, and LY. A P-value≤0.01 was considered to be statistically significant. A cut-off value defined by ROC curve analysis was found for each statistically significant texture parameter. The diagnostic accuracy was obtained for each texture parameter with AUC ≥ 0.5. The agreement between each texture parameter and histology was calculated using the Cohen's kappa coefficient.
The mean kappa values were 0.61, 0.34, 0.26, 0.17, and 0.48 for LY, EM, WT, PA, and benign vs. malignant lesions respectively. Long zone emphasis cut-off values >1.870 indicated EM with an accuracy of 81 % and values >2.630 revealed LY with an accuracy of 93 %. Long run emphasis values >1.050 and >1.070 indicated EM and LY with a diagnostic accuracy of 79% and 93% respectively.
Long zone emphasis and long run emphasis texture parameters allowed the identification of LY and the differentiation between benign and malignant lesions. WT and PA were not accurately recognized.
腮腺病变在形态学表现、表观扩散系数（ADC）值和时间/强度曲线类型上存在重叠。本研究旨在评估扩散加权成像纹理分析在鉴别腮腺良恶性病变、鉴别多形性腺瘤（PA）、Warthin 瘤（WT）、上皮恶性肿瘤（EM）和淋巴瘤（LY）中的作用。
对 54 例腮腺病变（19 例 PA、14 例 WT、14 例 EM 和 7 例 LY）的 ADC 图图像进行纹理分析。采用方差分析（ANOVA）检验来估计良恶性病变之间的差异，以及 PA、WT、EM 和 LY 之间的纹理特征差异。P 值≤0.01 被认为具有统计学意义。通过 ROC 曲线分析为每个具有统计学意义的纹理参数定义一个截断值。对于每个纹理参数，AUC≥0.5 的诊断准确性。使用 Cohen's kappa 系数计算每个纹理参数与组织学之间的一致性。
LY、EM、WT、PA 和良性 vs. 恶性病变的平均 kappa 值分别为 0.61、0.34、0.26、0.17 和 0.48。长区强调的截断值>1.870 提示 EM，准确率为 81%；截断值>2.630 提示 LY，准确率为 93%。长运行强调值>1.050 和>1.070 提示 EM 和 LY 的诊断准确率分别为 79%和 93%。
长区强调和长运行强调纹理参数可用于识别 LY，并区分良恶性病变。WT 和 PA 不能准确识别。
